The fundamental concept behind artificial neural networks (ANNs) is to create computational models inspired by the structure and function of the human brain's biological neural networks. Their primary purpose is to learn complex patterns and relationships from data to perform tasks that typically require human intelligence, such as pattern recognition, classification, and prediction. By processing information through interconnected layers of nodes (neurons), they effectively simulate a simplified version of the brain's cognitive and decision-making processes. This bio-inspired approach allows AI systems to tackle problems that are difficult to solve with traditional rule-based programming.

A. Improve the user interface: While AI can enhance user interfaces, this is a specific application of the technology, not the primary, foundational purpose of neural networks themselves.

B. Increase computational power: Neural networks are computationally intensive and require significant processing power to train and operate; they do not inherently increase it.

D. Minimize maintenance costs: Developing, training, and maintaining complex neural network models can be resource-intensive and costly, making cost minimization a secondary benefit at best, not the primary goal.

1. Goodfellow

I.

Bengio

Y.

& Courville

A. (2016). Deep Learning. MIT Press. In Chapter 1

"Introduction

" the authors state

"The field of deep learning was inspired by models of brain function... The core idea of deep learning is to build computational models that are composed of multiple processing layers that learn representations of data with multiple levels of abstraction

mimicking how the brain perceives and understands the world." (p. 1-2).

2. Jain

A. K.

Mao

J.

& Mohiuddin

K. M. (1996). Artificial neural networks: A tutorial. Computer

29(3)

31-44. The paper defines ANNs as systems "which are intended to interact with the objects of the real world in the same way as biological nervous systems do." (p. 32). https://doi.org/10.1109/2.485891

3. Stanford University. (n.d.). CS231n: Deep Learning for Computer Vision. Course Notes

Module 1: Neural Networks. The notes describe neural networks as being "originally motivated by the goal of modeling biological neural systems

but have since diverged and become an engineering approach to machine learning." This highlights the foundational goal of emulating biological intelligence.